Update app.py

app.py

@@ -1,3 +1,4 @@
+%%writefile app.py
 import streamlit as st
 import tensorflow as tf
 import numpy as np
@@ -134,7 +135,7 @@ def perform_object_detection(image, model, device):
         if scores[i] >= 0.75:
             x1, y1, x2, y2 = boxes[i]
             if (int(labels[i])-1) == 1 or (int(labels[i])-1) == 0:
-                color = (0, 0, 255)
+                color = (0, 255, 0)  # Green bounding box
                 label_text = f'Region {i}'
 
                 # Scale coordinates to original image size
@@ -149,16 +150,16 @@ def perform_object_detection(image, model, device):
                 # Check if image has 4 channels (RGBA), convert to RGB
                 if cropped_image.shape[-1] == 4:
                     cropped_image = cv2.cvtColor(cropped_image, cv2.COLOR_RGBA2RGB)
-
+                else:
+                  cropped_image = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
                 # Resize cropped image
                 resized_crop = resize_to_square(cropped_image)
-                cropped_images.append(resized_crop)
+                cropped_images.append([i,resized_crop])
                 detected_boxes.append((x1, y1, x2, y2))
 
                 # Draw bounding box
-                cv2.rectangle(result_image, (x1, y1), (x2, y2), color, 3)
-                cv2.putText(result_image, label_text, (x1, y1 - 10),
-                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+                cv2.rectangle(result_image, (x1, y1), (x2, y2), color, 1)
+                cv2.putText(result_image, label_text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1, cv2.LINE_AA)  # Yellow text, smaller font
 
     return Image.fromarray(result_image), cropped_images, detected_boxes
 
@@ -216,26 +217,26 @@ def main():
                     # Process each detected region
                     class_names = ['10', '6.5', '7', '7.5', '8', '8.5', '9', '9.2', '9.5', '9.7']
                     all_predictions = []
-
-                    for idx, cropped_image in enumerate(cropped_images):
+                    all_image=[]
+                    for idx, cropped_image in cropped_images:
                         processed_image = preprocess_image(cropped_image)
                         prediction = classification_model.predict(
                             np.expand_dims(processed_image, axis=0)
                         )
                         top_predictions = get_top_predictions(prediction, class_names)
-                        all_predictions.append(top_predictions)
-
+                        all_predictions.append([idx,top_predictions])
+                        all_image.append(cropped_image)
                     # Store in session state
                     st.session_state.predictions = all_predictions
-
+                    st.session_state.image = all_image
                 except Exception as e:
                     st.error(f"Error during processing: {str(e)}")
 
     with col2:
         st.subheader("Classification Results")
         if st.session_state.predictions is not None:
-            for idx, predictions in enumerate(st.session_state.predictions):
-                st.markdown(f"### Region {idx + 1}")
+            for idx, predictions in st.session_state.predictions:
+                st.markdown(f"### Region {idx}")
 
                 # Display main prediction
                 top_class, top_confidence = predictions[0]
@@ -278,12 +279,16 @@ def main():
                       key=f"selectbox_correct_grade_{idx}"
                   )
 
-                  # Chỉ thực hiện khi người dùng đã chọn giá trị trong selectbox
-                  if correct_grade:
+                  # Kiểm tra xem đã tải lên hay chưa
+                  if f"uploaded_{idx}" not in st.session_state:
+                      st.session_state[f"uploaded_{idx}"] = False
+
+                  # Chỉ thực hiện khi người dùng đã chọn giá trị và chưa tải lên
+                  if correct_grade and not st.session_state[f"uploaded_{idx}"]:
                       st.info(f"Đã chọn màu đúng: {correct_grade}")
 
                       # Resize hình ảnh xuống 256x256
-                      resized_image = Image.fromarray(cropped_image).resize((256, 256))
+                      resized_image = Image.fromarray(st.session_state.image[idx]).resize((256, 256))
                       temp_image_path = generate_random_filename()
 
                       # Lưu tệp resize tạm thời
@@ -295,8 +300,11 @@ def main():
                       if isinstance(cloudinary_result, dict):
                           st.success(f"Hình ảnh đã được tải lên thành công cho màu {correct_grade}")
                           st.write(f"URL công khai: {cloudinary_result['upload_result']['secure_url']}")
+                          # Đánh dấu trạng thái đã tải lên
+                          st.session_state[f"uploaded_{idx}"] = True
                       else:
                           st.error(cloudinary_result)
+
         else:
             st.info("Upload an image to see detection and classification results")